| commit | 9e99f3c4c3bec42299fa5e48a0cb3bc3aea264be | [log] [tgz] |
|---|---|---|
| author | Matthias Boehm <mboehm7@gmail.com> | Mon May 20 20:15:35 2024 +0200 |
| committer | Matthias Boehm <mboehm7@gmail.com> | Mon May 20 20:15:35 2024 +0200 |
| tree | 1ad6c579512237e0e81b9ee2812c0e23ed1af8f6 | |
| parent | 2972d6df5f2453e091343b59708343a4c562f185 [diff] |
[SYSTEMDS-3696] New sliceLineDebug built-in function for usability This patch adds a new sliceLineDebug function to present the top-k worst-slides returned from sliceLine (slicefinder) in a human readable format. This is the output for the Salaries dataset: sliceLineDebug: -- Slice #1: score=0.4041683676825298, size=248.0 ---- avg error=6.558681888351787E8, max error=8.524558818262574E9 ---- predicate: "rank" = "Prof" AND "sex" = "Male" -- Slice #2: score=0.3731763935666855, size=42.0 ---- avg error=8.271958572009121E8, max error=4.553584116646141E9 ---- predicate: "rank" = "Prof" AND "yrs.since.phd" = 31.25 -- Slice #3: score=0.3675193573989536, size=125.0 ---- avg error=6.758211389786526E8, max error=8.524558818262574E9 ---- predicate: "rank" = "Prof" AND "discipline" = "B" AND "sex" = "Male" -- Slice #4: score=0.35652331744984933, size=266.0 ---- avg error=6.307265846260264E8, max error=8.524558818262574E9 ---- predicate: "rank" = "Prof"
Overview: SystemDS is an open source ML system for the end-to-end data science lifecycle from data integration, cleaning, and feature engineering, over efficient, local and distributed ML model training, to deployment and serving. To this end, we aim to provide a stack of declarative languages with R-like syntax for (1) the different tasks of the data-science lifecycle, and (2) users with different expertise. These high-level scripts are compiled into hybrid execution plans of local, in-memory CPU and GPU operations, as well as distributed operations on Apache Spark. In contrast to existing systems - that either provide homogeneous tensors or 2D Datasets - and in order to serve the entire data science lifecycle, the underlying data model are DataTensors, i.e., tensors (multi-dimensional arrays) whose first dimension may have a heterogeneous and nested schema.
| Resource | Links |
|---|---|
| Quick Start | Install, Quick Start and Hello World |
| Documentation: | SystemDS Documentation |
| Python Documentation | Python SystemDS Documentation |
| Issue Tracker | Jira Dashboard |
Status and Build: SystemDS is renamed from SystemML which is an Apache Top Level Project. To build from source visit SystemDS Install from source
